Mathematical Modeling of a Programmable Decision Network to Simulate the Socio-Economic Environment

Mathematical modeling of decision networks is crucial in simulating the socio-economic environment, especially as these systems grow increasingly complex. Such models allow for a structured and quantitative analysis of the myriad factors and interactions that define modern economies and societies. These models enable researchers and policymakers to predict outcomes, identify optimal strategies, and evaluate the potential impacts of various decisions. This is particularly important in addressing intricate issues such as market dynamics, social welfare, and policy implementation, where traditional analytical methods fall short. Moreover, mathematical models facilitate the testing of hypothetical scenarios, providing valuable insights into how changes in one part of the system might ripple through the entire network. This paper proposes mathematical modelling of a new programmable decision network that allows simulating the socio-economic environment as composed of dynamically interacting components and relationships. The proposed network model, thanks to its programmability, can have a more comprehensive and deeper performance than the rest of the available traditional decision networks. As a result, these models are indispensable tools for understanding and managing the complexities of contemporary economic and social landscapes, ultimately aiding in the formulation of informed, effective policies and strategies.

• influence diagrams
• socio-economic environment
• market dynamics
• social welfare (welfare economics)
• policy evaluation
• scenario-based testing
• economic modeling

• Complex Systems and Decision Making
• Cognitive Science and Mapping
• Bayesian Modeling and Causal Inference
• influence diagrams
• socio-economic environment
• market dynamics
• social welfare (welfare economics)
• policy evaluation
• scenario-based testing
• economic modeling
• Management Science and Operations Research
• Artificial Intelligence
• Decision Sciences
• Computer Science